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Spatially-Constrained Similarity Measurefor Large-Scale Object Retrieval.

Xiaohui Shen, Zhe Lin, Jonathan Brandt

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary

    This study introduces a spatially-constrained similarity measure (SCSM) to improve object retrieval by adding spatial information. SCSM enhances accuracy for image search, including mobile product searches, outperforming existing methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Image Processing

    Background:

    • Object retrieval using bag-of-words models lacks spatial information, limiting effectiveness.
    • Existing spatial constraint methods are often too strict or too loose for general application.

    Purpose of the Study:

    • To propose a novel spatially-constrained similarity measure (SCSM) for robust object retrieval.
    • To develop a re-ranking method using k-nearest neighbors (k-NN) to refine search results.
    • To adapt SCSM for mobile product image search, including instance extraction and identification.

    Main Methods:

    • Developed a new spatially-constrained similarity measure (SCSM) using a voting-based method with inverted files.
    • Integrated object localization within the retrieval process using SCSM.
    • Introduced a k-NN re-ranking method for refining initial search results.
    • Adapted SCSM with an iterative algorithm for mobile product image search.

    Main Results:

    • SCSM effectively handles object rotation, scaling, viewpoint changes, and appearance deformation.
    • SCSM achieves simultaneous object localization during retrieval without post-processing.
    • SCSM significantly outperforms other spatial models, and k-NN re-ranking surpasses state-of-the-art query expansion methods.
    • The adapted SCSM robustly localizes and extracts product instances in mobile queries, drastically improving retrieval accuracy.

    Conclusions:

    • SCSM offers a superior approach to incorporating spatial information for object retrieval and localization.
    • The k-NN re-ranking method provides effective refinement of search results.
    • The adapted SCSM demonstrates significant improvements in mobile product image search accuracy.